WiMi Optimized Cloud Task Scheduling in Cloud Computing Using Group Intelligence Algorithms

WiMi Hologram Cloud announced that it optimized cloud task scheduling using group intelligence algorithms. A group intelligence algorithm is a computational method based on the behavior of groups in nature, which can demonstrate powerful search and optimization capabilities in solving complex problems by simulating the interactions and collaborations of individuals in a group. Using group intelligence algorithms to solve cloud task scheduling problems can improve task execution efficiency and resource utilization.

Group intelligence algorithms are a class of optimization algorithms that simulate the behavior of groups of organisms in nature, such as ant colony algorithms and particle swarm algorithms. These algorithms find the global optimal solution by simulating the collaboration and competition mechanism of biological groups. In cloud task scheduling, the use of population intelligence algorithms can view tasks and resources as individuals in a group, and find the optimal task scheduling solution through collaboration and competition among individuals. This can fully utilize the resources in the system, improve the task execution efficiency, reduce the waiting time, and lower the energy consumption and cost of the system.

Cloud task scheduling using group intelligence algorithms can meet users' needs, improve the response speed of the system, reduce the cost, and improve resource utilization. The group intelligence algorithm can be applied to different aspects of cloud task scheduling, such as task allocation, task scheduling, and task execution.

For example, cloud tasks are scheduled using particle swarm optimization. The PSO algorithm simulates the flight behavior of birds in a flock by constantly adjusting the position and speed of each bird in the flock to find the optimal solution. In cloud task scheduling, each task can be considered as a particle, the position of each particle indicates the virtual machine to which the task is assigned and the velocity indicates the execution speed of the task. By constantly updating the position and velocity of the particles, the optimal task scheduling solution can be found to improve task execution efficiency and resource utilization. The particle swarm algorithm is an optimization algorithm that simulates the foraging behavior of a flock of birds. In cloud task scheduling, the task can be regarded as the target that needs to be foraged by the flock of birds, and the cloud computing resources are regarded as the path of the flock of birds. The particle swarm algorithm searches for the optimal task scheduling scheme by simulating the position and speed adjustment of the bird flock during the search process. Specifically, each particle represents a task allocation scheme and adjusts its position and speed according to its own historical optimal position and the flock's optimal position. The PSO algorithm includes initializing the particle swarm, evaluating the fitness, updating the speed and position, and updating the global optimal solution and individual optimal solution.

First, a group of particles need to be initialized, each representing a task scheduling scheme. Some initial particles can be generated randomly or specified empirically. For each particle, its adaptation value needs to be calculated to evaluate its degree of superiority. The fitness value can be determined based on the task completion time, resource utilization, and other indicators. The higher the fitness value, the better the task scheduling scheme for the particle. Then the particle's speed and position are updated according to the particle's current speed and position, as well as the global optimal solution and the individual optimal solution. By adjusting the velocity and position, the particle can move closer to the global optimal solution to search for the optimal solution. For each particle, its individual optimal solution and global optimal solution need to be updated. The individual optimal solution is the best task-scheduling solution in the history of the particle itself, and the global optimal solution is the best task-scheduling solution in the whole particle swarm. Using the PSO algorithm can continuously search and optimize the cloud task scheduling scheme to improve the performance and efficiency of the system.

Source: WiMi Hologram Cloud media announcement